Comparison of Feature Extraction Techniques: A Case Study on Myocardial Ischemic Beat Detection

Transcription

1 International Journal of Pure and Applied Mathematics Volume 119 No , ISSN: (on-line version) url: Comparison of Feature Extraction Techniques: A Case Study on Myocardial Ischemic Beat Detection H.S.Niranjana Murthy 1, Dr.M.Meenakshi 2, 1 Ramaiah Institute of Technology, Bangalore-54, India, hasnimurthy@rediffmail.com 2 Dr.Ambedkar Institute of Technology, Bangalore-56, India, meenakshi_mbhat@yahoo.com Abstract This paper presents and compares the results of four feature extraction techniques for detecting Myocardial Ischemic Beats from ECG signal. The feature extraction techniques are based on morphological analysis, statistical analysis, principal component analysis (PCA) and independent component analysis (ICA) of ECG beat. The extracted features are used as inputs to Artificial Neural Network (ANN) classifier, Support Vector Machine (SVM) classifier and K nearest neighbor (KNN) classifier. The performance of all feature extraction techniques are validated and compared on ECG signals acquired from physiobank database in terms of positive prediction accuracy, classification accuracy and sensitivity. The experimental results have confirmed that highest testing classification accuracy of 96.85% is resulted from ANN classifier with ICA based features. This signifies that the ICA based feature extraction technique has immense potential than other techniques in diagnosing myocardial ischemia. I. INTRODUCTION Myocardial ischemia is a cardiac abnormality which affects the heart and the blood vessels. The primary cause for myocardial ischemia is the condition called atherosclerosis, which results in constriction of coronary arteries which limits the passage of oxygen rich blood to the heart and consequently leads to heart attack. Myocardial ischemia shows changes in ST-segment level and T wave alternance of ECG [1]. This condition occurring for a short time may lead to reversible effects leading to recovery of cardiac tissue, whereas the ischemia which persists for long period of time leads to death of heart cells resulting in heart stroke. Therefore, it is very much crucial for development of efficient classification algorithms for detecting myocardial ischemic beats from ECG for averting heart attacks. The quest for automated prognosis of myocardial ischemia has resulted in development of various classification algorithms based on feature extraction techniques in time and frequency domain. One of the preliminary methods of detecting myocardial ischemia is by comparison of morphological feature sets of ST segments with a standard reference ST set [2]. The authors have reported classification accuracy of 83.14% and adopted the rules relating the morphology of previous beats and future trends. For myocardial ischemic beat detection in long duration ECG recordings, the automated technique based on association rules was developed and the authors have reported 93% specificity and 87% sensitivity with European ST-T database records [3]. Statistical features such as maximum value, mean and primary ST deviation was used in conjunction with SVM classifier for improving accuracy. But the specificity was improved at the cost of sensitivity [4]. The PCA and Elman neural network was adopted for reducing the dimensions of morphological features, which showed promising results in classifying arrhythmias [5]. The comparison of performances of various classification algorithms have been reported in literature. These algorithms include neural network, wavelet transform, fuzzy cluster, and principal component analysis. Also, the simple classifiers such as linear discriminants, K-nearest neighbour [6] and composite classifiers including ANN, spectral coherence analysis and SVM have been extensively applied on ECG signal for diagnosing arrhythmia. Further, cascading of neural network classifier modules was carried out, which showed promising results in enhancing accuracy of arrhythmia detection from ECG [7]. It can be seen from the above references that some significant work has been done in diagnosing the myocardial ischemia with various classifiers based on features generated by different approaches. But majority of the work is confined to single type of feature extraction technique. This restricts the classification accuracy and to alleviate this drawback, the current work recommends development and comparison of various feature extraction techniques to identify the efficient myocardial ischemia classifier. Also, the results of all the above references showed the possibility of detecting myocardial ischemia with a maximum accuracy up to 90%. For the further improvisation of accuracy of detection, this work explores in the direction of developing an automated diagnosis of myocardial ischemic beats by using feature extraction techniques namely morphological feature extraction, statistical feature generation and integrating PCA with classifiers in which PCA is used for dimensionality reduction of input features. Further, for accurate classification of ischemic beats, the investigation is carried in the direction of developing a feature extraction based on ICA. The organization of this paper is as follows. Section 2 presents the proposed methodology for Ischemia classification which includes ECG signal preprocessing, various feature extraction techniques and different classifiers. Next, experimental results of classifying ischemic beats from normal beats of ECG signals are 1389

2 International Journal of Pure and Applied Mathematics highlighted in section 3. Lastly, conclusions are drawn in section 4. II. METHODOLOGY Entire work of classifying myocardial ischemic beats from normal beats of ECG signal involves three stages namely preprocessing, feature vector generation and classification as depicted in fig. 1. The preprocessing stage includes denoising, QRS detection and delineation of RT segment. cardiac ischemia, as confirmed by the literature. In the electrical cardiac cycle, the RT segment of the ECG beat represents the time from the ventricular depolarization to the end of the corresponding repolarisation. The main aim of this algorithm is to prepare compact description of the RT segment, composed of the ST segment and the T wave. Fig. 2. Method of Morphological feature extraction Fig. 1. Complete scheme of ECG processing and myocardial ischemic beat classification A. Preprocessing of ECG The ECG signals obtained from Physionet database is denoised by wavelet based thresholding technique with coif2 wavelet function and rigrsure thresholding rule [8]. After denoising, segmentation of ECG signals is carried between R-R intervals since most of the diagnostic information lies between R-R interval segments. The R-R interval segments of ECG signal is extracted by QRS complex identification and R peak detection. Although there are many different QRS detection techniques available, this work uses Pan-Tompkins algorithm. The annotation of ECG beats provides information regarding normal and ischemic beats. After fragmenting the ECG signal between R-R intervals, RT segment is extracted on which feature extraction techniques are applied to constitute feature vectors. B. Morphological Feature Extraction It is established that acquiring the samples between the R- T interval of ECG waves as feature values facilitate the best representation of the cardiac ischemia from ECG signal. This is due to the fact that ST-segment deviation and T wave alternance are the indicators of probable occurrence of The feature generation is accomplished by capturing samples between R-R intervals which relate to R-T interval for ECG beats. This is realized by using a rectangular window which is formed by 120 samples and moving it continuously over the entire detected QRS complexes to capture the RT segment as depicted in fig. 2. This corresponds to a window of 480 ms (120 samples at 250 Hz sampling frequency). The detected RT segments are put in the matrix form which is used as input of classifiers for segregating the myocardial ischemic beats and normal beats. C. Statistical Feature Extraction After RT segment detection, discrete wavelet transform is used for decomposing it into coefficients. For every ECG beat segment, D3, D4 and A4 coefficients are computed. The statistical features selected for feature extraction are mean, peak, root mean square, standard deviation (SD), minimum, skewness and crest factor (CF) kurtosis. Also, the non-dimensional features chosen for feature extraction from RT segment are clearance factor, shape factor & impulse factor (IF) as indicated in Table 1. The statistical features extracted by RT segments of ECG beats are different from each other. These statistical features are fed as input to classifiers for detecting ischemic beats. D. Feature Extraction based on PCA Fig.3 shows the flow chart of the proposed methodology for detecting myocardial ischemic beats from ECG signal using dimensionality reduction by applying PCA. 1390

3 International Journal of Pure and Applied Mathematics TABLE I STATISTICAL FEATURES EXTRACTED The RT segments delineated from ECG beats contain all information regarding morphological alterations like STsegment variance and T wave alternance, which are clear indicators of probable occurrence of myocardial ischemia. The effective performance of myocardial ischemia classifier depends on the dimensionality reduction of huge quantity of feature vectors [9]. In this work, PCA is applied on the RT segments of ECG beats resulting in vector of Principal Components (PC). The first four PC vectors are chosen due to the fact that the two foremost principal components represent the low-frequency components and last two PCs represent the high frequency components of RT segment. Fig. 3. Flow chart of cardiac ischemia detection by application of PCA E. Feature Extraction based on ICA In the proposed system for ECG classification, the ECG beat samples between RT intervals is extracted at first. Secondly, ICA feature vectors are constructed from ECG beat samples and then these ICA projected beats are decomposed by wavelet packet transformation (WPD). The ICA bases are estimated by the fast fixed point algorithm. ICA feature vector is normalized before performing wavelet packet decomposition. Four levels Wavelet packet decomposition is used to decompose the input signals into frequency bands. Out of these decomposed frequency bands, only three frequency bands are chosen since these bands are useful in extracting clinically useful information from the signal. Within each of these frequency bands, there are critical bands which are decomposed into coefficients by further applying wavelet packet transform. These wavelet coefficients are used for extracting features which are used for classifying ischemic beats from normal beats. The signal features are chosen based on prior knowledge of the characteristic of signals to the classifier [10]. A diversity of feature vectors have been utilized in earlier works for this purpose, including signal bandwidth, spectral centroid, signal energy, zero-crossing rate and frequency spectral coefficients. Three features including mean, standard deviation and entropy are computed from the wavelet coefficients of each sub band for classifying ischemic beats as in (1), (2) and (3). N 1 i M i wi( k) N (1) i k 1 i k 1 ( ) 2 i i N 1 i Stdi w k w (2) N L h l 2 h l (3) En ( ) Log ( ) i i i l 0 Where w i (k) denotes the k th coefficient of the i th sub-band of wavelet packet transform, where i=1,2,3, N i is the number of coefficients in i th sub-band, and k = 1,2,.,N i. h i is normalized values of wavelet coefficients at w i sub-band, and L is the order of decomposition levels. The above discussed feature extraction stage results in the formation of a 9-dimensional feature vector, which are fed as inputs to classifiers. F. ANN, SVM and KNN Classifiers The flexible configuration, good representational capabilities and various training algorithms has made multilayer perceptron (MLP) network a more appropriate classifier model [11]. MLP neural network classifiers are based on supervisory learning, which require a target response to be trained. They are generally used for pattern classification due to their ability to map any input pattern to target with single hidden layer. During training the ANN classifier, the weights and bias values are tuned so that the actual output from the ANN classifier meets the target values as close as possible. The best ANN architecture is obtained by trial and error technique and the neural network 1391

4 International Journal of Pure and Applied Mathematics configuration is characterised by the number of hidden neurons. The test set is presented after the MLP classifier is trained. In the current work, the MLP classifier is trained by adopting Levenberg-Marquardt back propagation algorithm. SVM is a supervised learning technique used for classification & regression [12]. An SVM model is a depiction of data sets as spots in space, correlated so that the datasets of the separate types are separated by a marginal space that is as broad as possible. SVM reduces the classification error and at the same time maximizes the marginal space. At first, the SVM classifier maps the input vectors into an upper dimensional space and then performs classification. K nearest neighbours is a non-parametric technique which stores all the available feature vectors and classifies them based on a similarity measure. KNN classifier requires a distance function and a positive integer K [13]. In KNN classifier, a case is assigned to the class by a majority vote of its neighbours measured by a distance function. The most commonly used distance metric is Euclidean distance [14]. III. RESULTS AND DISCUSSIONS Fig. 4 depicts simulated result of ECG signal denoising resulted by wavelet based threshold technique applied over the record e0603 [15]. Further, QRS complex and R peak detection is accomplished by techniques explained in section II (A). rectangular window formed by 120 samples and moving it continuously over the entire QRS complexes of ECG signal. These extracted RT segment samples are placed in matrix with rows indicating the ECG beats and columns indicating 120 samples. A feature vector of dimension 3108 x 120 is formed with 16 data files acquired from European ST-T datasets of MIT-BIH database Fig. 5. RT interval samples segmentation of ECG Beats of record e0603 The statistical features computed with technique discussed in section II (C) from the wavelet coefficients of ECG beats is shown in table 2. TABLE II STATISTICAL FEATURES EXTRACTED FROM WAVELET COEFFICIENTS OF ANNOTATED BEATS OF RECORD E0603 FROM ECG DATABASE Wavelet Coefficients ECG Beat types Extracted Features Sub bands D 3 D 4 A 4 Normal Beat Mean Standard Deviation RMS Peak Minimum Skewness Kurtosis Crest Factor Clearance Factor Shape Factor Impulse Factor Cardiac Ischemic Beat Mean Standard Deviation RMS Peak Minimum Skewness Kurtosis Crest Factor Clearance Factor Shape Factor Impulse Factor Fig. 4. QRS complex and R peak detection stages The feature vectors are created by extracting samples of RT-interval of ECG beat. Fig. 5 depicts the segmentation of the RT interval samples of ECG signal record no. e0603m of European ST-T dataset. This is achieved by using a Table 3 shows the extracted principal components from RT segments of ECG beats of an exemplary record e0603. In ICA based feature extraction technique, RT interval segment of ECG beats are projected on the bases to construct the ICA feature vectors. These ICA projected beats are decomposed into 4 level frequency bands by wavelet 1392

5 International Journal of Pure and Applied Mathematics packet decomposition as discussed in section II (E). Out of sixteen frequency bands, only three frequency bands are chosen which contains clinically useful information for further analysis. Next, the coefficients are derived from these frequency bands which results into generation of 17 wavelet coefficients respectively for each frequency band. The stacked 51 coefficients resulted from wavelet packet decomposition are utilized for computing the nine features, which includes mean, standard deviation and entropy for each frequency band as discussed in section II (E). Table 4 depicts the extracted features from wavelet coefficients of the above selected frequency bands. TABLE III FOUR PRINCIPAL COMPONENTS EXTRACTED FROM ECG BEATS OF ECG ECG Beat types Normal Beats Cardiac Ischemic Beats Beat No. RECORD E0603 Principal Components (PCs) PC1 PC2 PC3 PC4 Beat Beat Beat Beat Beat Beat Beat Beat Beat Beat Beat Beat Beat Beat Beat Beat Beat Beat Beat Beat TABLE IV EXTRACTED FEATURES FROM WAVELET COEFFICIENTS OF THREE FREQUENCY BANDS OF ECG BEATS FOR RECORD E0603 ECG Beat types Beat No. Sub band i =1 i =2 i = 3 Normal Beats Mean Standard Deviation Entropy Cardiac Mean Ischemic Standard Beats Deviation Entropy The performance evaluation of MLP neural network architectures is carried out by changing hidden layer neurons. The performance of SVM model is investigated by using different kernel functions. Similarly, the performance of KNN classifier is analyzed by varying the value of threshold K number. Further, the performance of the proposed ANN, KNN & SVM classifier models are reviewed by computing the Sensitivity (SE), positive prediction accuracy (PPA) and Accuracy (AC) of classification. A total of 3108 ECG beats across 16 data files of MIT-BIH database are used for extracting statistical feature vectors. The feature vectors extracted from 2424 annotated ECG beats are used for training set, feature vectors from 404 ECG beats are used for validation set and remaining feature vectors of 280 beats are used for test set. Fig. 6 indicates the comparison of classification accuracy of MLP, KNN & SVM classifiers. From the comparison charts depicted in fig. 6, it is evidential that the MLP neural network model based on proposed ICA based feature extraction method demonstrates improved percentage classification accuracy compared to other classification models. Fig. 7 shows the variation of performance indices of MLP classifier with respect to the variation of hidden neurons. It is inferential from the results that MLP architecture with 10 hidden neurons has performed best with highest percentage of PPA, sensitivity and accuracy. Fig. 8 depicts the variation of classification accuracy of MLP, SVM and KNN classifiers across the datasets chosen from MIT-BIH database. The results confirm that MLP classifier has shown best classification accuracy over entire dataset ( % ) ANN Classifier SVM Classifier KNN Classifier Morphological features Statistical features PCA based features ICA-WPD based features Fig. 6. Comparison of classification accuracy of ANN, SVM and KNN classifiers with various feature extraction techniques 16 Hidden neurons 12 Hidden neurons 10 Hidden neurons 8 Hidden neurons ( % ) PPA Accuracy Sensitivity Fig. 7. Comparison of performance indices at different MLP architectures for ICA based features 1393

6 e0103 e0104 e0108 e0127 e0133 e0147 e0155 e0166 e0204 e0211 e0304 e0403 e0411 e0501 e0602 e0603 % Accuracy International Journal of Pure and Applied Mathematics MLP SVM KNN MIT BIH data files Fig. 8. Comparison of % accuracy of classifiers across datasets of MIT- BIH database for ICA based features IV. CONCLUSION Four feature extraction techniques namely morphological features, statistical features, and features based on PCA and ICA were proposed and investigated for classifying myocardial ischemic beats. In this work, myocardial ischemic beat classification is carried out by using ANN, SVM and KNN classifiers and classifier efficiency is evaluated. The result indicates that the ANN model based on ICA based feature extraction has outperformed with classification accuracy of 96.85%. This accuracy is significantly high in comparison with SVM and KNN classifiers. The work evidently points out the enhanced accuracy in diagnosing myocardial ischemia and hence reduction in mortality rate by combining ANN with features extracted by ICA. [9] Ghorbanian.P, Ghaffari.A, Jalai.A, Nataraj.C, Heart Arrhythmia detection using continuous wavelet transform and principal component analysis with neural network classifier, Computing in Cardiology, IEEE publication, pp , [10] Jamal Saeedi, Seyed Mohammad Ahadi, Karim Faez, Robust voice activity detection directed by noise classification, Signal, Image and Video Processing, vol. 9, Issue 3, pp , [11] H.S.Niranjana Murthy and M.Meenakshi, Multivariate prediction of coronary heart disease based on ANN technique, ICRAES, Proceedings of International Review of Applied Biotechnology and Biochemistry, sep. 2014, Vol. 2, pp [12] Burges C.J.C, A tutorial on SVM for pattern recognition, Data Mining and Knowledge Discovery, 1998, Vol. 2, pp [13] Indu Saini, Dilbag Singh and Arun Khosla, QRS detection using K- Nearest Neighbor algorithm and evaluation on standard ECG databases, Journal of Advanced Research, pp , [14] Boshra Bahrami and Mirsaeid Hosseini Shirvani, Prediction and Diagnosis of Heart Disease using Data Mining Techniques, JMEST, Vol. 2, pp , [15] Goldberger A.L, Amaral LAN, Glass L, Housdorff J.M, Ivanov PCh, Mark R.G, Mietus J.E, Moody G.B, Peng C.K, Stanley H.E, 2000, PhysioBank, PhysiToolkit, and PhysioNet; Components of a New Research Resource for Complex Physiologic Signals, Circulation 101(23): e215-e220[circulation on Electronic Pages; (Accessed on August 2017). REFERENCES [1] Channer.K and Morris.F, ABC of Clinical Electrocardiography: Myocardial Ischaemia, Biomedical Journal, Vol.324, pp , [2] Gu Young Jeong, Kee-HoYu, Myoung Jong Yoon and EijiInooka, ST shape classification in ECG by constructing reference ST set, Medical Engineering and physics, vol.32, pp , [3] Papaloukas.C, Fotiadis.D.I and Michalis.L.K, An Association Rule Mining-Based Methodolgy for Automated detection of Ischemic ECG beats, Biomedical Engineering, IEEE Transactions, Vol.53, pp ,2006. [4] Zimmerman. M.W and Povinelli.R.J, On Improving the Classification of Myocardial Ischemia using Holter ECG Data, IEEE Computers in Cardiology, vol.31, pp , [5] Mohamad.F.N, MSA Megat Ali, AH Jahidin, MF Saaid, MZH Noor, Principal component analysis and arrhythmia recognition using Elman neural network, Proceedings of 4 th IEEE control and system graduate research colloquium, pp ,2013. [6] Jekova I., B. Bortolan, I.christov, Assessment and comparison of different methods for heartbeat classification, Medical Engineerig and Physics, Vol. 30, pp , [7] Javadi M, R.Ebrahimpour, A. Sajedin, S.Faridi, S.Zakemejad, Improving ECG classification accuracy using an ensemble of neural network modules, PLOS one, Vol.6, pp.1-13, [8] H.S.Niranjana Murthy and M.Meenakshi, Optimum Choice of Wavelet Function and Thresholding Rule for ECG Signal Denoising, Proceedings of IEEE International Conference on Smart Sensors and Systems, pp.1-5,

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